scholarly journals On the Computation of Concept Stability Based on Maximal Non-Generator for Social Networking Services

2020 ◽  
Vol 10 (23) ◽  
pp. 8618
Author(s):  
Jie Gao ◽  
Fei Hao ◽  
Doo-Soon Park

The concept stability measure under the Formal Concept Analysis (FCA) theory is useful for improving the accuracy of structure identification of social networks. Nevertheless, the stability calculation is an NP-complete task which is the primary challenges in practical. Most existing studies have focused on the approximate estimate to calculate the stability. Therefore, we focus on introducing the Maximal Non-Generator-based Stability Calculation (MNG-SC) algorithm that directly deals with accurate stability calculation to pave the way for FCA’s application in structures identification of social networks. Specifically, a novel perspective of stability calculation by linking it to Maximal Non-Generator (MNG) is first provided. Then, the equivalence between maximal non-generator and lower neighbor concept is first proved, which greatly improves scalability and reduces computational complexity. The performed experiments show that the MNG-SC outperforms the pioneering approaches of the literature. Furthermore, a case study of identifying abnormal users in social networks is presented, which demonstrates the effectiveness and potential application of our algorithm.

Author(s):  
A.C.C. Coolen ◽  
A. Annibale ◽  
E.S. Roberts

This chapter reviews graph generation techniques in the context of applications. The first case study is power grids, where proposed strategies to prevent blackouts have been tested on tailored random graphs. The second case study is in social networks. Applications of random graphs to social networks are extremely wide ranging – the particular aspect looked at here is modelling the spread of disease on a social network – and how a particular construction based on projecting from a bipartite graph successfully captures some of the clustering observed in real social networks. The third case study is on null models of food webs, discussing the specific constraints relevant to this application, and the topological features which may contribute to the stability of an ecosystem. The final case study is taken from molecular biology, discussing the importance of unbiased graph sampling when considering if motifs are over-represented in a protein–protein interaction network.


Author(s):  
Marie-Aude Aufaure ◽  
Bénédicte Le Grand

Concept lattices have been widely used for various purposes in many different applications since the 1980s. Recent applications of Formal Concept Analysis include extensions of traditional FCA applications such as data and text mining, machine learning and knowledge management. Progress has also recently been made in software engineering, Semantic Web and databases. New applications have also emerged in the fields of healthcare, ecology, biology, agronomy, business and social networks. This article presents example of successful applications of FCA for Social Networks Analysis. We show the benefit of FCA solutions, as well as their combination with semantics and topology-based approaches. We conclude by presenting FCA-based visualization solutions and open challenges for FCA in the context of large and dynamic data.


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